was used to test both models. Birth cohort (birth year: 1890-1929; 1930-1949; 1950-1969; >=1970) was a stronger predictor of alcohol dependence than was age (see also: Grucza et al., 2008 41), and hence was selected along with sex, GWAS array indicator, and the first four ancestral principal components (as in a prior study by 34) as covariates in the model. In GWS regions, conditional analyses were performed by including the most significant variant in the region as a covariate to evaluate whether a single locus explained the association signal. The trans-ancestral (EA+AA) meta-analysis was performed using inverse-variance weighting in METAL 42. As implemented in METAL, genomic control, which was estimated by comparing the median test statistics to those expected by chance alone, was applied to the GWAS of COGA AA and COGA EA. For the trans-ancestral meta-analysis (EA+AA), genomic control was applied to the standard errors of the effect sizes. All genomic control estimations were implemented in METAL. Only GWS variants (p <5E-8) were evaluated in replication samples. As we tested seven individual criteria for the tertiary analyses, a matrix of the phenotypic correlations between these criteria in the EA participants (Supplemental Table 1B) was spectrally decomposed using matSpD 43,44
